{"id":157882,"date":"2005-01-01T00:00:00","date_gmt":"2005-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/automatically-constructing-a-corpus-of-sentential-paraphrases\/"},"modified":"2018-10-16T20:06:00","modified_gmt":"2018-10-17T03:06:00","slug":"automatically-constructing-a-corpus-of-sentential-paraphrases","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/automatically-constructing-a-corpus-of-sentential-paraphrases\/","title":{"rendered":"Automatically Constructing a Corpus of Sentential Paraphrases"},"content":{"rendered":"<div class=\"asset-content\">\n<p>An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released MicrosoftResearch Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released MicrosoftResearch Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-157882","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"Asia Federation of Natural Language Processing","msr_edition":"Third International Workshop on Paraphrasing 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